Sub-cell level, multi-layer degradation detection, diagnosis and recovery

ABSTRACT

Outage detection and recovery in heterogeneous networks may benefit from sub-cell level, multi-layer degradation detection, diagnosis, and recovery. For example, cell outage detection and cell outage compensation may benefit from appropriate use of sub-cell level and multi-layer diagnosis and recovery techniques applied to degradation and outage scenarios. A method can include self-healing at least one first cell in a network or at least one second cell in the network. The network includes the at least one first cell and the at least one second cell. The at least one first cell and the at least one second cell have at least partially overlapping coverage. The self-healing can include a network device, such as a network management device, applying at least one of multi-layer reasoning to integrate information of different cell layers or sub-cell level reasoning to consider fine-granular location information. The self-healing can be based on location information.

BACKGROUND

1. Field

Outage detection, diagnosis, and recovery in heterogeneous networks maybenefit from sub-cell level, multi-layer degradation detection,diagnosis, and recovery. For example, cell outage detection, cell outagediagnosis, and cell outage compensation may benefit from appropriate useof sub-cell level and multi-layer diagnosis and recovery techniquesapplied to degradation and outage scenarios.

2. Description of the Related Art

Cell outage detection and recovery methods may exist for the long termevolution (LTE) of the third generation partnership project (3GPP).Conventional cell outage detection does not, however, consider locationinformation for performance management data, upon whichdegradation/outage detection is based.

Alarm correlating techniques also may exist, aimed at reducing thevolume of alarm flow. The input for such conventional techniques is thealarms. The alarms, however, contain no detailed location information.Thus, these techniques cannot help to provide a more accurate diagnosisof the problem or improve recovery planning.

SUMMARY

A method according to certain embodiments includes self-healing at leastone first cell in a network or at least one second cell in the network.The network includes the at least one first cell and the at least onesecond cell. The at least one first cell and the at least one secondcell have at least partially overlapping coverage. The self-healingincludes a network device applying at least one of multi-layer reasoningto integrate information of different cell layers or sub-cell levelreasoning to consider fine-granular location information. Theself-healing is based on location information.

A non-transitory computer readable medium, according to certainembodiments, is encoded with instructions that, when executed inhardware, perform a process. The process includes self-healing at leastone first cell in a network or at least one second cell in the network.The network includes the at least one first cell and the at least onesecond cell. The at least one first cell and the at least one secondcell have at least partially overlapping coverage. The self-healingincludes a network device applying at least one of multi-layer reasoningto integrate information of different cell layers or sub-cell levelreasoning to consider fine-granular location information. Theself-healing is based on location information.

An apparatus, in certain embodiments, includes self-healing means forself-healing at least one first cell in a network or at least one secondcell in the network. The network includes the at least one first celland the at least one second cell. The at least one first cell and the atleast one second cell have at least partially overlapping coverage. Theself-healing means includes applying means for applying at least one ofmulti-layer reasoning to integrate information of different cell layersor sub-cell level reasoning to consider fine-granular locationinformation. The self-healing is based on location information.

In certain embodiments, an apparatus includes at least one processor andat least one memory including computer program instructions. The atleast one memory and the computer program instructions are configuredto, with the at least one processor, cause the apparatus at least toself-heal at least one first cell in a network or at least one secondcell in the network, and to apply at least one of multi-layer reasoningto integrate information of different cell layers or sub-cell levelreasoning to consider fine-granular location information. The networkincludes the at least one first cell and the at least one second cell.The at least one first cell and the at least one second cell have atleast partially overlapping coverage. The self-healing is based onlocation information.

BRIEF DESCRIPTION OF THE DRAWINGS:

For proper understanding of the invention, reference should be made tothe accompanying drawings, wherein:

FIG. 1 illustrates a method of cell degradation detection and diagnosis.

FIG. 2 illustrates a heterogeneous multi-layer network deployment.

FIG. 3 illustrates an architecture of a network management systemaccording to certain embodiments.

FIG. 4 illustrates correlation of detected information according tocertain embodiments.

FIG. 5 illustrates sub-cell level detection of failures according tocertain embodiments.

FIG. 6 illustrates a scenario in which a single radio cell layer isconsidered according to certain embodiments.

FIG. 7 illustrates multi-layer reasoning in a scenario where a microcell is completely covered by a macro cell according to certainembodiments.

FIG. 8 illustrates multi-layer reasoning in a scenario without accuratelocation information according to certain embodiments.

FIG. 9 illustrates multi-layer reasoning in combination with sub-cellreasoning for a macro cell in degradation according to certainembodiments.

FIG. 10 illustrates multi-layer reasoning in combination with sub-cellreasoning for a macro cell in outage according to certain embodiments.

FIG. 11 illustrates the architecture of a system according to certainembodiments.

FIG. 12 illustrates a method of self-healing according to certainembodiments.

FIG. 13 illustrates an apparatus according to certain embodiments.

DETAILED DESCRIPTION:

Management tasks such as planning, initial configuration, optimization,troubleshooting can be performed using planning and monitoring tools.Automated ways of network management also exist, for example,Self-Organizing Networks (SON) can perform some management tasksautomatically. The domains covered by SON include self-configuration,self-optimization and self-healing. Self-configuration can includeauto-connectivity and initial as well as operational-time configurationof network elements. Self-optimization can include handling efficientresource allocation and automatically following the varying demand fromsubscribers and the changing environment. Self-healing can aim atdetecting anomalies in the network, diagnosing faults,correlating/reducing alarms to improve alarm quality, automaticallycompute, and execute compensation and recovery actions. Thus,self-healing can generally refer to the overall process (includingdetection, diagnosis, and recovery/repair), or to any sub-set of theoverall process (for example, only detection or only diagnosis or onlydiagnosis and recovery/repair).

Often, only cell outage detection (COD) and cell outage compensation(COC) are mentioned as SON self-healing use cases. However, in certainembodiments described herein, cell degradation detection (CDDe) anddiagnosis (CDDi) are considered: both contain the outage case and thecase that the cell is still able to provide a certain level of servicebut its performance is below the expected level by an amount clearlyvisible to the subscribers as well. That is, a cell outage is a specialcase of degradation meaning that the cell is unable to provide anyacceptable service, often meaning that users are not able to connect toit and there is no traffic in the cell at all. Furthermore, thisapproach clearly analytically separates the detection, such as detectingrelevant incidents in the network, and diagnosis functionality, such asidentifying the root cause of an incident.

CDDe and CDDi can be based on performance monitoring (PM) data and/oralarm reporting. Performance data includes failure counters such as calldrop, unsuccessful radio access channel (RACH) access, etc. as well asmore complex key performance indicators (KPIs) such as traffic load,which can be monitored and profiled to describe the “usual” behavior ofusers and detect if patterns are changing in a direction that indicatesa problem in the network. In order to analyze the root cause of asuspected fault, the different KPIs can be correlated with each other torecognize the characteristic imprints of different faults. FIG. 1illustrates a method of cell degradation detection and diagnosis.

As can be seen from FIG. 1, measurement collection can occur at 110.These measurements can be provided to diagnosis and detection 120.Specifically, degradation detection 125 can find problematic cells witha low false positive rate. The degradation detection 125 can alsoprovide input to the root cause diagnosis 127, which can infer a rootcase of detected degradation. Alternatively, at 123, diagnosis can beskipped. Subsequently, at 130, solution deployment can be initiated inresponse to the detected degradation.

Thus, in certain embodiments, a solution is needed to recover the celltaking the root cause of the problem into consideration. This can bereferred to as cell degradation recovery (CDR). Possible actions forrecovery include cell reset, which may solve issues caused by software(SW) error, fallback to “last known good” configuration orreconfiguration of configuration management (CM) parameters, celltilt/azimuth optimization, and the like. Root cause analysis andrecovery can, for example, be done manually by an operator'stroubleshooting experts and can take a long time, especially if radiore-planning is also needed. The coverage/capacity missing due to thefault may, therefore, need to be compensated until the repair isdeployed and it is proven to solve the problem. This is the scope ofcell outage compensation (COC) that reconfigures the radio parameters,for example, transmission power and tilt, of cells adjacent to thefailure to try to fill the lost coverage/capacity.

Cellular networks may be complex due to the number of deployed networkelements, the co-existence and co-operation of multiple technologies(e.g., GSM, EDGE, HSPA, I-HSPA, LTE), and multiple layers of the sametechnology (e.g., LTE femto/pico/macro cells). Moreover, operatorsregularly deploy equipment from multiple vendors, thereby increasing theneed for interoperability. This co-existence of various generations ofradio technology and the increasing requirement for high data rates candrive radio network deployments towards heterogeneous network (HetNet)layouts. In heterogeneous networks, a certain geographical area iscovered by several radio resource layers, where different “resourcelayers” refer to either different radio access technologies (RATs)(“multi-RAT”) or different cell layers within one RAT (“multi celllayer”). For the “multi cell layer” scenario co-channel or nonco-channel deployments can be considered. In the following “multi-layer”refers to both cases and their combinations.

FIG. 2 illustrates a heterogeneous multi-layer network deployment. Ascan be seen in FIG. 2, a geographic area can be covered by a variety ofwide area, medium area, hot spots, and indoor networks. The indoornetworks can include, WiFi, LTE Femto, and HSPA+ Femto. The hot spotscan be, for example, TD-LTE. The medium area networks can include LTEand GSM, whereas the wide area networks can include HSPA+. Of course,these are just examples of the networks that may be in place withoverlapping coverage.

Heterogeneous networks complicate SON, as the number of base stationsgreatly increases, as well as the number of different equipment vendorsfor a single deployment. For example, the separate management ofindividual femto cells may not even be possible or desired from anetwork management (NM)/domain management (DM) level as theiravailability cannot be predicted (customers may switch them on/off).Multi-layer deployments on the other hand provide new opportunities forcell outage detection, compensation and recovery as the additional layermay compensate for the outage of another resource layer. However,conventional self-healing focuses on a single cell within a single radioaccess technology (e.g., an LTE macro cell) to detect if it is in anoutage and how the outage can be compensated (COC) by adapting some ofthe neighbor cells to provide coverage on the degraded area.

In order to assess the performance of the network elements, more thanjust network-side performance metrics can be utilized; in LTE, there ispossibility to use certain measurements from user equipment to getperformance data. This is referred to as minimization of drive tests(MDT). User equipment (UE) measurements can be used for OAMautomation/SON and versatile data can be accessed via MDT. For example,in some systems there may be a capability to request positioning dataalong with MDT measurements. This function of MDT can be utilized byself-optimization and self-healing processes to take the location ofcertain events (e.g., RLF) into account when analyzing the root cause ofthe problem and making compensation and recovery plan.

Geo-location methods with which the location of a symptom or a fault canbe tracked include network assisted positioning, global positioningsystem (GPS) (with built-in receivers at either the base transceiverstation (BTS) (for time synchronization and/or accurate positioning) orthe UEs (e.g., for personal navigation)) and digital subscriber line(DSL) line identifier (ID) (linked to a subscriber home address) in caseof customer premise equipment (CPE) devices like femto cells. In LTE,advanced radio technologies such as inter-cell interference coordination(ICIC), multiple input multiple output (MIMO) and beamforming alsoprovide the base station with positioning information; using ICIC, itcan be determined whether a UE is at the cell edge or not; MIMO andbeamforming provides azimuthal information. The LTE timing advanceparameter computed by the eNodeB can give an approximate distance of theUE from the BTS (the granularity can be, for example, 78 meters).

Not only geo-location related physical positioning but also abstractmetrics such as topological information can be utilized in certainembodiments. For example, two cells can be related and thus theirdetection and recovery actions can be correlated and coordinated if theyare topologically close, even if their radio coverage has no overlappingarea. As an example, if there is erroneously low traffic in severalcells that are not adjacent but share a common transport service, e.g.,same microwave aggregation ring, same access VLAN ID, etc., the problemmay be with transport and not radio. Therefore, it may be useless oreven harmful to initiate any radio related compensation action. Instead,transport diagnostic or optimization tools can be triggered if theproblem is not a transient transport congestion but there is a fault.

Conventional networks mainly use alarms as notification of certainproblems towards the operator. KPI values may also be taken into accountwhen considering whether an alarm is to be raised. However, alarms andKPIs conventionally only indicate degradations on the level of cellgranularity (e.g., cell ID contained in the alarm), which means that anycompensation or recovery action based on the received alarms can onlyassume that the indicated cell as a whole is degraded/non-optimallyconfigured/etc. This may lead to an “over-compensation” in some cases.For example, if only a small part of the cell is affected by adegradation, an overcompensation may be to adjust parameters of alladjacent cells to compensate for the lost coverage. Usually, radiocoverage problems such as shadowing do not affect the whole coveragearea of a cell but only parts of it. Additionally, problems affectingonly a small area of a cell may not be detected at all because of thelittle influence on the overall cell performance.

In heterogeneous network deployments, for example in densely populatedurban areas, it can be the case that although a resource layer is inoutage, there are still enough resources in other layers to provideservice. Thus, no compensation (such as tilt/power change in neighboringcells) may be needed. Such compensation actions themselves have animpact on the coverage/quality of service (QoS) around the area, sounnecessary use of such actions can have a ripple effect. For example,in urban cases, outdoor users may have at least a minimum level ofservice from the neighboring cells even without any reconfiguration.

Conventional CDDe/CDDi methods operate at the cell level, makingdetection and diagnosis based on one cell/layer. However, faultincidents may have uncorrelated root causes across the layers. Also,decision about a compensation/recovery action is made based on onecell/layer, which may lead to conflicting actions. For example, if thereis a problem with cells in different layers at the same location, theroot cause of the problem may be an environmentally induced degradationlike shadowing or a problem with shared transport link than separatecoinciding faults of all resource layers. On the other hand, if a cellreports problem but other cells at same location (in another layer)report no failure, it may be the problem of the original cell only.

Certain embodiments enhance current cell degradation detection (CDDe),cell degradation diagnosis (CDDi), and cell degradation recovery (CDR)methods in order to enable efficient self-healing in heterogenousnetwork scenarios. This may be accomplished, for example, through thefollowing. Multi-layer reasoning can create an integrated heterogeneousnetwork resource view that integrates the information of different celllayers. Additionally, sub-cell level reasoning can considerfine-granular location information of network measurements obtained byMDT or similar facilities.

The methods according to certain embodiments can perform sophisticatedreasoning that can provide self-healing behavior in heterogeneousnetworks, and can contrast to conventional systems which performsingle-layer, cell-level reasoning. Certain embodiments provide severalfunctional blocks useful for enabling multi-layer and sub-cell levelreasoning, as well as a framework to effectively combine them, and anarchitecture that leverages their full advantages. Features of certainembodiments can be integrated into the CDDe, CDDi, and CDR components asshown in Table 1, below. In the following discussion, bothfunctionalities are described in more detail and their advantages areoutlined.

TABLE 1 Multi-layer and sub-cell level reasoning in CDDe, CDDi, and CDRSub-cell level reasoning Multi-layer reasoning CDDe Sub-cell leveldetection n/a CDDi Sub-cell level diagnosis Multi-layer diagnosis CDRSub-cell level recovery Multi-layer recovery (compensation)(compensation)

Sub-cell level reasoning can enable the detection, diagnosis, andcompensation on a sub-cell level instead of a conventional cell-levelspatial scope through the usage of measurements, such as MDTmeasurements, and other sources of network data featuring thegeo-location of a taken measurement. Furthermore, a tolerance of thelocation may be added according to the source of the geo-locationinformation. For instance, geo-locations taken by GPS have a smalltolerance of several meters whereas measurements which can be onlylocated to be within a cell have a larger tolerance, which may be, forexample, the size of the cell.

The use of sub-cell level reasoning can lead to detection and diagnosisbeing able to localize failures with a fine-grained resolution smallerthan the size of the radio cell. This can help to identify and adjustfor environmentally-induced failures, like shadowing, which may impedejust a part of the cell. Additionally, the precise localization offailures can enable a focused recovery. For instance, conventional COCmethods may solely compensate cell degradations uniformly: they changethe configuration of all surrounding cells to cover a circular areaaround the degraded cell. However, if just a part of the cell is impededthis approach can have an unnecessary impact on the non-degraded partsof the considered cell. These impacts can include interference in caseof a co-channel deployment or reduced capacity and increased overhead ina non-co-channel deployment. Using sub-cell-level reasoning, therecovery can specifically direct the compensation to the location of thefailure in the cell, thus reducing negative impacts of compensation.

Multi-layer reasoning can extend detection, diagnosis, and recovery totake into consideration different cell layers in heterogeneous networks.On the one hand, multi-layer reasoning allows the diagnosis to analyzeand correlate failures on different layers, which can improve thediagnosis result. For instance, if cells at the same location aredegraded then it may be caused by some environmental issue. However, ifonly one of these cells shows degradation then the degradation may becaused by some cell-intrinsic issue. As another example, if a micro cellshows a significant increase in traffic while an overlapping macro cellshows a decrease, this behavior may point to a degradation of the macrocell if other explanations like an ongoing load balancing procedure canbe ruled out. Furthermore, the information from different layers canalso be used to improve the localization of a failure. If a micro celland an overlapping macro cell show the same (for example,environmentally induced) problem then both problems may have the samecause, and that this cause is located within the coverage area of thesmaller micro cell.

On the other hand, the recovery of cell degradations and outages canalso benefit from information of other layers. Because a specificlocation can be covered by several cell layers, recovery actions (forexample, compensation) can be more focused and thus avoid causing“collateral damage” to other healthy cells. For example, the system canavoid causing additional interference when compensating in a co-channeldeployment. For instance, a degraded cell can cover an area that is alsocovered by cells of a different layer. In this case, a compensation ofthe degradation may not be necessary since the other cells can take overthe traffic in this area. It is also possible that a specific area isshowing a coverage hole due to some shadowing. If there are smallheterogeneous network cells like micro cells in the proximity of thecoverage hole then these cells may be reconfigured to extend theircoverage to precisely the problematic area. As a result, the problem canbe compensated with reduced impact compared to compensating with arelatively huge macro cell.

FIG. 3 illustrates an architecture of a network management systemaccording to certain embodiments. More particularly, FIG. 3 depicts thefunctional architecture of certain embodiments, including three majorfunctional blocks: sub-cell level detection; multi-layer, sub-cell leveldiagnosis; and multi-layer, sub-cell level recovery. These blocks can bevariously implemented in terms of hardware, either by following thefunctional blocks or by departing from them. The following descriptionoutlines these functional blocks in more detail. Not all embodimentsnecessarily include all functional blocks shown in FIG. 3. For example,the system may, in certain embodiments, use only one or the other ofmulti-layer reasoning and sub-cell level reasoning.

Sub-cell level detection can be responsible for evaluating the “health”status of a network cell or element by analyzing measurements andalarms. In case degradation is detected, sub-cell level detection canprovide descriptive alarms which are annotated with a location andlocation tolerance when being forwarded to the diagnosis. Sub-cell leveldetection can solely concentrate on a single cell and, so, a singlenetwork layer. That is, it does not take advantage of multi-layerreasoning. The functional block of sub-cell level detection can includetwo interacting sub-components.

A location unification component can annotate all data from the networkelement (NE) (for example, measurements and alarms) with geo-locationinformation, for example, a geo-location and a tolerance. For example,the location unification component can utilize the geographical cellarea information from the network planning database. For instance, MDTmeasurements can be annotated with coordinates that identify where theyhave been taken. The location unification component can also add ageographical tolerance to the data. To provide the tolerance, thelocation unification component can be configured to identify the sourceand significance of the measurements. Here the significance can refer,for example, to the accuracy of the source of geo-location information.In the same way, the location unification component can classify somealarms as being related to the whole cell. Thus, the locationunification component can annotate these alarms with the location andtolerance of the whole cell.

The detection component can take the annotated data, correlate thelocation information of the data (with one another) and analyze the dataanomalies. Some scenarios are discussed below in which correlation oflocation information from measurements can improve detection. The outputof the detection component can be a set of descriptive alarms fordetected problems. The locations of measurements can be correlated asshown in FIG. 4, which illustrates correlation of detected informationaccording to certain embodiments. That is, separate measurements 410,each with its own geo-location and tolerance can be correlated in orderto create one alarm 420 with an averaged location and tolerance. Inorder to implement this component, conventional detections methods canbe extended to correlate the locations of different measurements.

The multi-layer, sub-cell level diagnosis module can analyze thegeo-location annotated, descriptive alarms provided by the detectionmodule in order to come up with a root cause for the problem at hand. Incontrast to conventional diagnostic approaches, multi-layer, sub-celllevel diagnosis can take advantage of the location information of alarmsto provide improved accuracy and precision of diagnosis. This extensioncan be provided through the interaction of two sub-components.

A location correlation component can take the annotated alarms from allcells of all layers and can group them according to their location. Thatis, alarms with overlapping alarm regions, which are determined by thegeo-location and the tolerance of the alarm, can be grouped together.Thus, one alarm can be assigned to more than one group can consequentlycan be present several times in various groups. For instance, if thereare two overlapping alarms in a cell (each alarm with a smalltolerance), the two alarms can be built into an alarm group. However, ifthere is a third alarm whose tolerance is the whole cell then an alarmgroup containing all three alarms can also be created. The set of alarmgroups can at least contain a group for each cell where a problemexists. The location correlation can, thus, be useful for multi-layerreasoning.

Subsequently, the diagnosis component (the components caninterchangeably be referred to as modules herein) can perform adiagnosis based on the alarm groups. Thus, the alarms in a group whichare within a cell-layer can be analyzed using classical intra-cell layerdiagnostic approaches. Additionally, inter-cell layer diagnosis methodscan be applied to the alarms. The inter-cell layer diagnosis methods cananalyze alarms in different cell-layers at a location and, thus, allowdistinguishing between cell intrinsic and environmental problems moreaccurately. Furthermore, the methods can also correlate the diagnosisresults for different alarm groups in order to localize a problem moreprecisely. The intra-cell layer diagnosis can either performedsequentially with the inter-cell layer analysis or in combinationtherewith. The diagnosis component can output possible, geo-locationannotated root causes for the problems in the network.

A multi-layer, sub-cell level recovery component or module can determinethe best way to recover from a degradation given a root cause diagnosedby the multi-layer, sub-cell level diagnosis component. Thus, themulti-layer, sub-cell level recovery component can consider thegeo-location and location tolerance of the problem and align theproblem's location with the geo-location of the radio cells in thenetwork. A goal of the multi-layer, sub-cell level recovery componentcan be to minimally change the normal operation of the non-faultynetwork parts. For instance, in COC a compensation of a cell outage bytuning the adjacent cells can create further problems in a ripple,domino, or ping-pong effect amongst cells. Hence, minimizing or avoidingcompensation can be used to avoid such effects. Several scenarios arediscussed below, in which compensation is focused in and the goal ofminimal change is accomplished.

The above examples may be particularly applicable to macro, micro, andpico cells. Femto cells are conventionally not visible to the networkmanagement system one-by-one; instead, they are conventionally managedthrough a femto-gateway. Enterprise femtos, where there are cellboundaries between femto cells, for example, are typically managed thisway. By contrast, individual femtos are traditionally separate in thesense that they have only macro-cell neighbors. For example, if an enduser buys a femto and install it at home, it is typically separateconventionally. Whether or not femtos are included in the networkmanagement described above, if there is a significant percentage offemtos in outage, this aggregate information can be be detected,localized (as much as possible), signaled and compensated. For example,certain embodiments consider statistics-based alarms in detection forthe femto scenario. That is, the femto management raises an alarm if,for example, 10% of all the femtos managed by it are faulty and thedetection takes this aggregated alarm into consideration.

Certain embodiments rely on particular functions and sources ofinformation. For example, the detection, diagnosis and recovery offailures in mobile networks are complex tasks that require a lot ofinformation about failures, their symptoms, and suitable recoveryoptions. Thus, the network performance and fault data as well as theexpert knowledge to interpret the data can be provided to the system.Moreover, fine grained compensation of coverage holes in a directedmanner may require an active antenna technology. For instance, anability to enlarge or reduce a cell's coverage area in a specificdirection may be useful.

In order to localize a fault on a sub-cell level, the system can beprovided with accurate and fine-grained geo-locations of measurements,for example, through MDT. Other sources of information regardingmeasurements are also permitted.

So that the system can take into consideration cells of different layersin the network, this topological data can be provided to the system.This topological data can also include accurate geo-locations of thenetwork elements and estimations of the area covered by the cells. Theinformation in this database can be computed from other information. Forexample, models can be used in which the cells are to assumed to becircles, simulators can be used to estimate coverage, or MDT-basedestimations can be used.

Sub-cell level and multi-layer reasoning can make self-healing in mobilenetworks more effective. On the one hand, the detection and diagnosis offailures may be more accurate and, on the other hand, the recovery offailures may be more directed. In the following, several scenarios arepresented that demonstrate how certain embodiments can achieve these orother advantages compared to conventional self-healing approaches, suchthose approaches that do not consider multi-layer or sub-cell locationinformation.

FIG. 5 illustrates sub-cell level detection of failures according tocertain embodiments. Sub-cell level reasoning can help to improve theaccuracy of the detection of failures by enabling the analysis of areassmaller than a cell. FIG. 5 depicts a scenario in which this abilityprovides significant advantages over conventional approaches. The upperportion of FIG. 5 shows that common detection methods performstatistical analysis of the whole cell 510. Hence, local coverageproblems, shown as solid FIG. 515, are hard to detect since their impacton the overall cell performance is less significant, as can be seen inthe bar chart on the right. Certain embodiments using sub-cell leveldetection, as shown in the lower part of FIG. 5, can analyze issues in asmaller area than the cell 520. Consequently, small coverage problems515 can have a significant impact on the performance in this sub-cellarea and, thus, may be easier to detect, as can be seen in the bar charton the right.

Furthermore, sub-cell level reasoning can also improve diagnosis andrecovery of failures, because the exact localization of problems canpermit directing recovery efficiently. FIG. 6 depicts a scenario with asingle radio cell layer, where a cell is partially degraded and is to becompensated. In the upper part, the conventional situation is shown.Suppose the operations, administration, and maintenance (OAM) systemdiagnoses a cell with degraded coverage, shown as central solid ellipse610 vs. its desired coverage, shown as dashed ellipse 620, leading to alocal coverage hole. Since there is no detailed location informationavailable, the system is not able to localize the coverage hole exactly,the coverage hole being defined by the vertical lines. Consequently, forcompensating the problem, the system conventionally assumes that thecoverage is reduced evenly around the cell center. Thus, conventionalcompensation algorithms increase the coverage area of the neighbor cellsuniformly, as depicted by larger ellipses 630 on right side. However,since the area right of the cell center is still covered by the faultycell, this behavior leads to an increased interference, indicated by aflash. Certain embodiments avoid this outcome as shown in the lowerpart. The diagnosis is enabled to accurately localize the coverage holeresulting from the degradation. This allows the system to direct thecompensation to this problematic area. Hence, the neighbor cell on theright hand side is not reconfigured and thus the undesired interferencecan be minimized.

Multi-layer reasoning can further be used by the diagnosis component ofcertain embodiments to infer a more accurate location of a failure incase there is no sub-cell level location information available. This canbe the case because either sub-cell level location information is notsupported by the NEs or because there is an insufficient amount oflocalized measurements, for example, via MDT. FIG. 7 depicts a scenariowhere a micro cell is completely covered by a macro cell. If both layerssuffer from an equal, externally induced problem, for example, shadowingby a high building, then one failure can be affecting both layers inparallel, depicted as center ellipse 710 in macro layer, and left-mostellipse 720 in micro layer. The system can correlate the data from bothlayers and induce that the problem must be somewhere in the coveragearea of the micro cell, shown by the vertical lines. Hence, the locationof the problem can be more accurately identified with respect to themacro cell than using a conventional approach.

Independently of the location information for failures provided by thediagnosis component, the recovery from these problems can be aided byusing all layers of the mobile network instead of just the affected celllayer. FIG. 8 depicts this in a scenario without accurate locationinformation, two cell layers in a co-channel deployment, and a degradedmacro cell, shown as center ellipse 810, causing a coverage hole,indicated by vertical lines, which has to be compensated. The upper partshows that conventional compensation procedures can solely use the macrolayer to resolve the issue. Hence, the coverage area of the adjacentcells is equally extended, shown as larger ellipses 820. Since there isa micro layer overlapping with the macro layer, this causes interferencein a co-channel deployment, indicated by flashes. The lower part of FIG.8, shows that certain embodiments allow for a better behavior. Althoughthe location of the coverage hole is not accurately determined, as shownby the vertical lines, the system can recover in a more focused way fromthe degradation by taking advantage of the other layers. On the onehand, the system can reason that the left part of the degraded cell doesnot have to be compensated at all since the two micro-layer cells arealready covering the problematic area. On the other hand, the system caninfer that compensation the right part of the degraded cell with themicro layer cell, shown as larger ellipse 830, is more precise thanusing the macro layer cell. As a result, certain embodiments allowreducing interference in such cases by either omitting unnecessarycompensations or performing compensations with the best situated cellsof all layers. Note that due to the inaccurate localization of thecoverage hole, the compensation can assume a uniform degradation of thecell causing interference on the right side.

Multi-layer, sub-cell level reasoning combines the advantages of bothfeatures. Thus, the functionality in the scenarios presented above canbe applied to a combined system as well. FIG. 9 illustrates thepotential of certain embodiments in a multi-layer, co-channel deploymentscenario where a degradation of a macro cell, shown as center ellipse910, can be accurately detected and diagnosed using precise locationinformation, whereas FIG. 10 depicts the same scenario for a macro cellin outage. In both figures, the upper part shows the behavior ofconventional detection, diagnosis, and recovery approaches. As in thetwo use cases before, the system, on the one hand, is not able toprecisely determine the location of the problem, shown as verticallines, and, on the other hand, does not consider other layers of thenetwork serving the same area. Consequently, in this example the usageof a compensation procedure, shown as larger ellipses 920 and 1020,causes interference, shown as flashes, in the network. Certainembodiments improve this situation. First, the sub-cell levellocalization, shown as vertical lines, of the problem allows the systemto compensate the coverage holes in a more directed way. Second, theconsideration of other layers and radio access technologies (RATs)allows the system to omit unnecessary compensation when a problematicarea is served by cells on other layers (FIG. 9), and to perform thecompensation more precisely by using smaller and better situated cells,shown as larger ellipses 1030, on other layers (FIG. 10). As a result,interference problems by the compensation can be reduced.

The architecture of certain embodiments, for example, the mapping of thefunctional blocks presented in FIG. 3 to the elements of a mobilenetwork, may be useful for leveraging its full potential. In oneexample, a 3-layered 3GPP architecture is considered. FIG. 11illustrates the architecture of a system according to certainembodiments. The architecture, as considered, includes a network elementlevel at the bottom, a domain management (DM)/Element Management (EM)level in the middle, and Network Management (NM) level on top. On theone hand, the lower a functional block is placed in this architecture,the more fine-grained data is available at smaller time intervals. Thescope of the data (the number of different network entities it comesfrom), however, decreases at lower levels of the architecture. On theother hand, the higher a functional block is placed, the broader is thescope of the data, which can be useful for multi-layer reasoning,because it integrates data from different entities. However, this meansthat more and potentially less granular data may be available. In thefollowing discussion, three architectural options for certainembodiments are presented:

First, a centralized architecture can assign the three functional blocksto the NM or DM level. Thus, the detection, diagnosis and compensationcomponents can be pooled at a central location. Hence, data from allnetwork elements can be transferred to the NM/DM level for analysis.This option has the advantage that, on the one hand, the diagnosiscomponent can easily analyze data from all network elements since alldata is centrally available, and, on the other hand, the recoverycomponent can easily perform a healing action since it solely hascontrol over the network. However, this approach can use a lot of datatraffic from the edges of the network to the NM level. Numerousfine-granular measurements can be transferred in order to provide thedetection component with sufficient data to be efficient. This trafficplaces a load on the network.

For the NM-level cases, this option can use open and standardizedinterfaces between DM and NM level for exchanging low level performancedata containing location information, for example, for MDT measurements,in order to be multi-vendor capable. For the vendor-specific DM-levelcase, no open interface is required.

In a second approach, a decentralized architecture can put thedetection, diagnosis, and recovery components on the network elements.Hence the functional blocks are highly distributed over the network. Anadvantage of this approach is that the analysis of the network data isperformed close to where it is created, thus, reducing traffic overhead.However, a complex data exchange between the NEs may be needed toprovide access for the diagnosis and recovery components to informationfrom several layers. Hence, with this solution multi-layer reasoning maybe omitted, or the network may be loaded with traffic between the NEs.Furthermore, if the recovery component in one NE has no control overother NEs, a complex coordination scheme can be implemented to avoidinteractions of adjacent recovery components with negative effects onnetwork performance. This architecture can utilize open and standardizedinterfaces for exchanging information about detection/diagnosis eventsin the form of alarms as well as recovery actions between different NEs.

A third, hybrid architecture can aim to leverage the advantages of thetwo former architectures by assigning each functional block to the mostsuitable network layer. As illustrated in FIG. 11, the detectioncomponent can be distributed to the NEs, whereas the diagnosis andrecovery components can reside centrally at the NM (or DM) level. Theadvantage of this assignment is that the detection is able to performits analyses close to the source of data, which may allow for a timelyand comprehensive investigation while minimizing the necessary networktraffic. The diagnosis and recovery components can benefit from thecentral deployment by having easy access to all data from all detectionfunctions on all network layers. Hence, the correlation of multi-layerdata is readily possible. Additionally, the full, central control of therecovery component over the network can ease the mitigation of failures,because no complex coordination of independent entities is necessary.

For the case of diagnosis and recovery being at the NM-level, theinterface between the detection and diagnosis functional blocks can bean extension of the Itf-N interface of the 3GPP architecture. Thisinterface can allow the detection components to send alarms, which areannotated with location and location tolerance data, to the centraldiagnosis component. Furthermore, this interface can decouple thedetection from the diagnosis. Thus, alarms from detection components bydifferent vendors can be correlated in the diagnosis. Moreover, concretedetection algorithms can be vendor specific and, thus, optimized for thespecific hardware of that vendor.

Another case is diagnosis being at the DM-level (due to vendorspecifics) and recovery being at the NM-level. This implies per domaindiagnosis components which notifies a unified recovery component at theNM-level based on standardized fault causes.

In summary, certain embodiments can improve existing CDDe, CDDi, and CDRmethods in a heterogeneous network environment by utilizing locationinformation for measurements and alarms provided by, for example, MDT ornetwork planning, and topological network data of all network layers.Concretely, the advantages of certain embodiments can include animproved accuracy of CDDe by providing a fine-grained sub-cell levelanomaly localization, an improved correctness of CDDi by consideringsub-cell level location information of alarms and correlating them overco-located cells, and an improved quality of CDR by considering theexact location of failures and execution of recovery actions on othernetwork layers.

Certain embodiments can even provide benefits when just one of thedimensions introduced in Table 1 is extended, such as when MDT data isnot available. Several scenarios are outlined above, in which certainembodiments improve network management by minimizing or avoidingreconfigurations of NEs as much as possible.

FIG. 12 illustrates a method of self-healing according to certainembodiments. The method of FIG. 12 can include self-healing at least onefirst cell, such as a larger cell or a cell of a first radio accesstechnology, in a network, such a heterogeneous network, or at least onesecond cell, such as a smaller cell or a cell of a second radio accesstechnology, in the network. The terms “larger” and “smaller” can referto the coverage area or coverage volume of the cells. The cells may beof the same approximate size and type or may be of different types.Thus, for example, as can be seen from FIG. 2, a heterogeneous networkcan have a variety of cells with overlapping coverage, from a very largecoverage for some macro cells to a coverage of only a few meters forsome indoor cells. Certain embodiments can also apply to embodiments inwhich each “cell” is the area covered by a respective remote radio headof a distributed base station. Larger and smaller cells are provided asan example of these various possibilities.

More than one larger cell or smaller cell (or combinations thereof orcombinations of other categories or types of cells) can also beself-healed. The term “self-healing” here refers to an automated processof network management in which a fault is detected, diagnosed, andrepaired. The repair can include compensation or the like. It is notrequired that a self-healing method include all of the detection,diagnosis, and repair. Instead, several self-healing methods can beperformed in coordination. For example, as shown in FIG. 11, a detectionmethod can be performed at the network element level, but the diagnosisand recovery (repair) can be performed at a network management and/ordomain management level. Each of these methods individually or incombination can be referred to as self-healing.

As shown in FIG. 12, the method can include, at 1210, identifying alocation of degradation and/or outage of a cell (for example, arelatively large cell, such as a macro cell, or a relatively small cell,such as a femto cell). The identification can be performedautomatically, without manual intervention. Moreover, the identificationcan be performed based on opportunistic measurements, rather than byintentional surveying. This detection can be performed by a networkelement (such as an access point) or by a network device such as anetwork management device. The self-healing can also include a networkmanagement device applying at least one of multi-layer reasoning tointegrate information of different cell layers, at 1230, or sub-celllevel reasoning to consider fine-granular location information, at 1220.The self-healing can include the network management device applying boththe multi-layer reasoning and the sub-cell level reasoning. The twoapproaches can be applied in sequence or in combination.

The self-healing, in any case, can be based on location information,such as the location of the degradation or outage. The locationinformation can also include relative location information of one layerof coverage with respect to another layer of coverage. In certainembodiments, the location information can include information regardingthe precise location of user equipment within the coverage area. Thelocation information can also include a tolerance of the locationinformation. The location information can include BTS measurements oflocation, or other location information, such as GPS data.

The sub-cell level reasoning can include processing networkmeasurements, which can be measurements of location obtained by networkelements or reported by user equipment.

The sub-cell level reasoning can be applied to at least one of sub-celllevel detection 1222, sub-cell level diagnosis 1224, and sub-cell levelrecovery 1226 or repair. The multi-layer reasoning can be applied to atleast one of multi-layer diagnosis 1232 and multi-layer recovery orrepair 1234.

Thus, the self-healing can include at least one of cell degradationdetection, cell degradation diagnosis, and cell degradation recovery,that is, for example, the self healing can include just cell degradationdetection, just cell degradation diagnosis, just cell degradationrecovery, or any combination of cell degradation detection, celldegradation diagnosis, and cell degradation recovery. When asub-cell-level fault is detected, sub-cell-level restoration can beperformed. The sub-cell level detection can provide descriptive alarmsthat are annotated with a location and location tolerance and thenforwarded to the diagnosis.

The self-healing can include, for example as an aspect of themulti-layer reasoning, comparing coverage of a macro-cell layer tocoverage of a micro-cell layer. Moreover, the self-healing can includecompensating coverage, or other characteristics, of a macro-cell layerbased on information regarding a micro-cell layer or compensatingcoverage, or other characteristics, of the micro-cell layer based oninformation regarding the macro-cell layer. Here, macro-cell layer issimply an example of the layer of a larger cell and micro-cell layer issimply an example of the layer of a smaller cell. FIG. 7, for example,illustrates how a macro cell layer coverage can be adjusted based onlocation information from the micro cell layer. This is just onenon-limiting example.

The multi-layer reasoning and the self-healing in general can includeinter-cell diagnosis, intra-cell diagnosis, and intra-cell layerdiagnosis. The inter-cell diagnosis, the intra-cell diagnosis, and theintra-cell layer diagnosis can be performed in sequence or incombination with one another.

FIG. 13 illustrates an apparatus, such as a network management device,according to certain embodiments. As shown in FIG. 13, a networkmanagement device 1310 may be equipped with at least one processor 1340,at least one memory 1350, including computer program instructions orcode, a transceiver 1360, an antenna 1370, and manager module 1380.There is no requirement that every network management device be soequipped. For example, the network management device can be equipped forwired communication only and consequently may not include antenna 1370.

The transceiver 1360 can be a transmitter, a receiver, both atransmitter and a receiver, or a unit that is configured both fortransmission and reception. The transceiver 1360 can be coupled tocorresponding one or more antenna(s) 1370, which may include adirectional antenna. As noted above, the antenna can be omitted incertain embodiments.

The at least one processor 1340 can be variously embodied by anycomputational or data processing device, such as a central processingunit (CPU) or application specific integrated circuit (ASIC). The atleast one processor 1340 can be implemented as one or a plurality ofcontrollers.

The at least one memory 1350 can be any suitable storage device, such asa non-transitory computer-readable medium. For example, a hard diskdrive (HDD) or random access memory (RAM) can be used in the at leastone memory 1350. The at least one memory 1350 can be on a same chip asthe corresponding at least one processor 1340, or may be separate fromthe corresponding at least one processor 1340. The memory 1350 caninclude at least one database.

The computer program instructions may be any suitable form of computerprogram code. For example, the computer program instructions may be acompiled or interpreted computer program.

The at least one memory 1350 and computer program instructions can beconfigured to, with the at least one processor 1340, cause a hardwareapparatus (for example, network management device 1310) to perform aprocess, such as any of the processes described herein (see, forexample, FIGS. 1 and 3-12).

Thus, in certain embodiments, a non-transitory computer-readable mediumcan be encoded with computer instructions that, when executed inhardware perform a process, such as one of the processes describedherein. Alternatively, certain embodiments of the present invention maybe performed entirely in hardware.

The manager module 1380 can be separate device configured to operate inconnection with the processor 1340 and memory 1350 or can be implementedby, for example, configuring the processor 1340 to perform one or moreroutines stored in memory 1350.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.In order to determine the metes and bounds of the invention, therefore,reference should be made to the appended claims.

GLOSSARY OF ABBREVIATIONS

BTS Base Station

CDDe Cell Degradation Detection

CDDi Cell Degradation Diagnosis

CDR Cell Degradation Recovery

COC Cell Outage Compensation

COD Cell Outage Detection

COR Cell Outage Recovery

CPE Customer Premise Equipment

CM Configuration Management

DM Domain Management

DLS Digital Subscriber Line

EM Element Management

FCAPS Fault, Configuration, Accounting, Performance, Security Management

GPS Global Positioning System

HSPA High-Speed Packet Access

ICIC Inter-Cell Interference Coordination

KPI Key Performance Indicator

MDT Minimization of Drive Tests

MIMO Multiple Input Multiple Output

NE Network Element

NM Network Management

OAM Operation, Administration and Maintenance

PCI Physical Cell ID

PM Performance Management

RACH Random Access Channel

RAT Radio Access Technology

SON Self-Organizing Networks

UE User Equipment

1. A method, comprising: self-healing at least one first cell in anetwork or at least one second cell in the network, the networkcomprising the at least one first cell and the at least one second cell,wherein the at least one first cell and the at least one second cellhave at least partially overlapping coverage, wherein the self-healingcomprises a network device applying at least one of multi-layerreasoning to integrate information of different cell layers or sub-celllevel reasoning to consider fine-granular location information, andwherein the self-healing is based on location information.
 2. The methodof claim 1, wherein the sub-cell level reasoning comprises processingnetwork measurements.
 3. The method of claim 1, wherein the self-healingcomprises the network device applying both the multi-layer reasoning andthe sub-cell level reasoning.
 4. The method of claim 1, wherein thesub-cell level reasoning is applied to at least one of sub-cell leveldetection, sub-cell level diagnosis, and sub-cell level recovery.
 5. Themethod of claim 1, wherein the multi-layer reasoning is applied to atleast one of multi-layer diagnosis and multi-layer recovery.
 6. Themethod of claim 1, wherein the self-healing further comprises at leastone of cell degradation detection, cell degradation diagnosis, and celldegradation recovery.
 7. The method of claim 1, wherein the self-healingis based on a tolerance of the location information.
 8. The method ofclaim 1, wherein the self-healing comprises sub-cell-level restorationwhen a sub-cell-level fault is detected.
 9. The method of claim 1,wherein the self-healing comprises comparing coverage of a macro-celllayer to coverage of a micro-cell layer.
 10. The method of claim 1,wherein the self-healing comprises compensating coverage or othercharacteristics of a layer of the larger cell based on informationregarding a layer of the smaller cell or compensating coverage or othercharacteristics of the layer of the smaller cell based on informationregarding the layer of the larger cell.
 11. The method of claim 1,wherein the multi-layer reasoning includes inter-cell diagnosis,intra-cell diagnosis, and intra-cell layer diagnosis.
 12. The method ofclaim 1, wherein the self-healing is based on location information thatincludes relative location of at least one network element of a firstlayer with respect to at least one network element of a second layer.13. The method of claim 1, wherein the self-healing is based on locationinformation that includes relative location of at least one networkelement of a layer with respect to at least one other network element ofthe layer.
 14. A non-transitory computer readable medium encoded withinstructions that, when executed in hardware, perform a process, theprocess comprising a method according to claim
 1. 15. An apparatus,comprising: self-healing means for self-healing at least one first cellin a network or at least one second cell in the network, the networkcomprising the at least one first cell and the at least one second cell,wherein the at least one first cell and the at least one second cellhave at least partially overlapping coverage, wherein the self-healingmeans comprises applying means for applying at least one of multi-layerreasoning to integrate information of different cell layers or sub-celllevel reasoning to consider fine-granular location information, andwherein the self-healing is based on location information. 16.-27.(canceled)
 28. An apparatus, comprising: at least one processor; and atleast one memory including computer program instructions, wherein the atleast one memory and the computer program instructions are configuredto, with the at least one processor, cause the apparatus at least toself-heal at least one first cell in a network or at least one secondcell in the network, the network comprising the at least one first celland the at least one second cell, wherein the at least one first celland the at least one second cell have at least partially overlappingcoverage, and to apply at least one of multi-layer reasoning tointegrate information of different cell layers or sub-cell levelreasoning to consider fine-granular location information, and whereinthe self-healing is based on location information.
 29. (canceled) 30.The apparatus of claim 28, wherein the at least one memory and thecomputer program instructions are configured to, with the at least oneprocessor, cause the apparatus at least to apply both the multi-layerreasoning and the sub-cell level reasoning.
 31. The apparatus of claim28, wherein the sub-cell level reasoning is applied to at least one ofsub-cell level detection, sub-cell level diagnosis, and sub-cell levelrecovery.
 32. (canceled)
 33. The apparatus of claim 28, wherein the atleast one memory and the computer program instructions are configuredto, with the at least one processor, cause the apparatus at least toperform degradation detection, cell degradation diagnosis, and celldegradation recovery.
 34. The apparatus of claim 28, wherein the atleast one memory and the computer program instructions are configuredto, with the at least one processor, cause the apparatus at least tobase self-healing on a tolerance of the location information. 35.-40.(canceled)